是一个关于序列分析以及比较统计的DOS程序的软件包，其中包括有距离建树方法和MP建树方法。

生物信息学培训教材（北京华大基因研究中心）经典的培训教程

关于生物信息学的资料 上面是进化算法在基因序列中的应用

33种TCGA肿瘤的拷贝数GISTIC2.0分析结果 单价29.9元，付款后私信给博主，博主分享网盘链接。

生信-国科大雁栖湖春季课程《使用生物信息学2：多组学数据整合和挖掘》知识点整理

森林草莓CENH3基因家族的生物信息学分析，马跃，张天龙，CENH3（centromere-specific histone H3 variant）是着丝粒特异性组蛋白H3变异体。在拟南芥中CENH3基因的改造植株与正常野生型杂交能够获得单倍体�

The Python code and supporting material, including example data files, are available as a single ZIP compressed archive. This must be uncompressed before use and will extract into a folder (directory) called "PythonForBiology", inside which the Python files, ending in ".py", and various sub-folders can be found. This arrangement of files and folders will allow the Python code to run directly from inside the uncompressed "PythonForBiology", i.e. the locations of any modules or data files mentioned in the code (and book) are specified relative to this location. The "examples" sub-folder contains all of the data files that are used as examples to support the Python code described in the book. The "databases" sub-folder relates to Chapter 20 and contains SQL and Python files sub-divided into sections to support both SQLite and MySQL database implementations. The "speedy" folder relates to Chapter 27 and contains code relevant to the binding of fast functions written in the C or Cython languages, including any files required for compilation. Many of the book chapters have a corresponding Python file containing the completed scripts and programs for that chapter. These files may be run directly as Python to test the code they contain. Note that several of these files will not work isolation, given that they import functionality from the others, which are assumed to be in the same folder. Chapters 1-4 and 10 do not have a corresponding Python file given that they only discuss the code in terms of short or incomplete fragments.

参考文献：MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction 目的：蛋白质二级结构预测。 上传原因：由于文献中的链接失效，因此将之前下载的开源源码上传。仅限学术研究使用。

生物工具 GTF.py 提供一个类，使您可以解析，重建仅由外显子组成的GTF或计算一些基本统计信息。 用法 从CLI 要仅从外显子重建完整的GTF（具有3个级别：基因，转录本和外显子），请使用以下命令，然后将重建的GTF打印到STDOUT GTF.py format {gtf_path} 要计算您的GTF（仅包含外显子的GTF）中的基因，转录本和外显子的数量，请使用： GTF.py stats {gtf_path} 从Python脚本 首先，导入GTF类。 此类提供了一种静态方法来解析您的文件： 您的GTF由3个级别的注释组成。 在这种情况下，请使用： from GTF import GTF for gene in GTF . parse ({ your GTF file }): print ( gene ) 您的GTF仅由外显子组成。 在这种情况下，请添加arg by

qPCR结果查看与分析1